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Datasets Overview

FinBrain provides 12 alternative datasets across equities and other asset classes, normalized and ticker-mapped for direct integration into research pipelines, trading systems, and visual analysis. Each dataset is documented on its own page with sample responses, code examples, and use cases.

Coverage

Section titled “Coverage”
MetricValue
Total tickers28,000+
Global markets20
Datasets12
Historical depth5+ years (varies by dataset)

Dataset Categories

Section titled “Dataset Categories”

We organize the dataset catalog into three categories based on the type of signal they provide.

Government & Regulatory

Section titled “Government & Regulatory”

Public disclosure data tied to government processes — congressional trading, lobbying activity, and federal contract awards. These datasets are difficult and time-consuming to aggregate independently and provide signals not found in price data alone.

DatasetDescriptionUpdate Frequency
House TradesUS House member trading activity from STOCK Act filingsAs filed
Senate TradesUS Senate member trading activity from STOCK Act filingsAs filed
Corporate LobbyingFederal LDA filings tracking corporate influenceQuarterly
Government ContractsFederal contract awards mapped to ticker symbolsDaily

Social & Consumer Intelligence

Section titled “Social & Consumer Intelligence”

Signals from public social platforms, news media, professional networks, and app ecosystems. Useful for monitoring narrative shifts, retail attention, and consumer-facing company performance.

DatasetDescriptionUpdate Frequency
News SentimentAI-generated sentiment scores from financial newsDaily
News ArticlesRecent financial news with source attributionReal-time
LinkedIn MetricsEmployee counts and follower growthWeekly
App Store RatingsiOS and Android app performanceWeekly
Reddit MentionsTicker mentions across investing subredditsEvery 4 hours

Market & Trading Signals

Section titled “Market & Trading Signals”

Quantitative forecasts and market activity data — price predictions, analyst views, options positioning, and insider transactions.

DatasetDescriptionUpdate Frequency
Price ForecastsStatistical price forecasts with confidence intervalsDaily
Analyst RatingsWall Street ratings, upgrades, downgrades, price targetsDaily
Put/Call RatiosOptions market sentiment and flowDaily
Insider TransactionsSEC Form 4 filings tracking insider activityReal-time

Common Patterns

Section titled “Common Patterns”

All FinBrain datasets share a consistent structure that makes them straightforward to integrate.

Standardized Response Envelope

Section titled “Standardized Response Envelope”

Every API response is wrapped in the same envelope:

{
  "success": true,
  "data": {
    "symbol": "AAPL",
    "name": "Apple Inc.",
    // dataset-specific fields
  },
  "meta": {
    "timestamp": "2026-04-18T12:00:00.000Z"
  }
}

This means the integration code for one dataset is structurally identical to the integration code for any other.

Ticker-Mapped at the Source

Section titled “Ticker-Mapped at the Source”

Every record is mapped to a stock ticker symbol at ingestion. You can query LinkedIn metrics, lobbying filings, government contracts, and Reddit mentions all by the same AAPL or MSFT symbol — no fuzzy matching, no name resolution, no manual joining.

Consistent Field Naming

Section titled “Consistent Field Naming”

Field names use camelCase across all datasets. Numeric values are returned as numbers (not strings). Dates use ISO 8601 format. This consistency matters when you’re joining datasets across endpoints in pipeline code.

Historical Depth

Section titled “Historical Depth”

Most datasets have 5+ years of historical data. A few newer series have shallower history and are being backfilled. Check individual dataset pages for exact coverage.

Ways to Access the Data

Section titled “Ways to Access the Data”

Every dataset is delivered through four interfaces. Choose the one that fits your workflow:

InterfaceUse Case
FinBrain TerminalVisual exploration, screening, ticker deep dives, no code
REST APIProduction pipelines, custom integrations, any language
Python SDKQuant research, backtesting, Jupyter workflows
MCP IntegrationLLM-powered research, AI assistants, semantic queries

The same data is available through every interface — pick whichever matches how your team works.

Next Steps

Section titled “Next Steps”